Must Know Deep Learning Application | Intellipaat

  • Self Driving Cars: Autonomous self-driving vehicles are a concept that is driven by deep learning. Deep Learning technologies are basically “learning computers” that pick up behaviour and response patterns through training and millions of data sets. Uber Artificial Intelligence Laboratories are creating self-driving cars for food delivery in addition to powering further autonomous vehicles as part of their strategy to diversify their business infrastructure. In contrast, Amazon has used drones to transport its goods in some parts of the world.
    The confusing problem with self-driving cars is something that the bulk of its creators are attempting to solve by subjecting them to a variety of scenarios to assure safe driving. They have working sensors that can recognise surrounding objects. To avoid traffic, the vehicle uses data from its cameras, sensors, geo mapping, and complex models. A well-known example is Tesla.
  • Natural Language Processing:NLP, or natural language processing, is a significant area where deep learning is demonstrating promising outcomes. It is the process that enables robots to learn and understand human language.

 

However, bear in mind that robots have a very tough time understanding human language. The alphabet, words, background, accents, writing, and other elements all work against machines correctly understanding or producing human language.

 

By teaching machines to respond appropriately to linguistic inputs, Deep Learning-based NLP addresses many of the difficulties associated with understanding human language.

  • Visual Recognition: Let’s say you’re looking through old photos or memories. Some of these may be printed, if you so want. The only way to accomplish this in the absence of metadata was through manual labour. The one most you could do is sort them by date, but often the metadata is missing from downloaded photos. On the other side, Deep Learning has made the task simpler. It can be used to organise images based on locations recognised in photos, features, a mix of people, events, dates, etc. Modern visual recognition algorithms with levels ranging from basic to complex are needed to identify features when looking for a specific photo in a library.
  • Fraud Detection: Fraud protection and identification is another intriguing use for deep learning; big players in the payment service industry are already working with it. For instance, PayPal uses predictive analytics technologies to identify and stop fraud. According to the company, analysing user behaviour sequences utilising neural networks’ long short-term memory structure improved anomaly identification by up to 10%. Every fintech company, banking app, insurance platform, and organisation that collects and uses sensitive data must have sustainable fraud detection methods. Fraud can become more foreseeable and hence preventable thanks to deep learning.

Personalisations: Today, every platform is seeking to deploy textbots to give users personalised, human-touched experiences. Deep Learning is helping e-commerce behemoths like Amazon, E-Bay, and Alibaba identify tremendous revenue potential during the holiday season and offer seamless personalised experiences like product suggestions, customised packaging, and discounts. Reconnaissance is carried out, even in more recent markets, by offering products, offers, or strategies that are more likely to appeal to consumer psychology and support the expansion of niche markets. Online self-service options are becoming more prevalent, and reliable procedures are bringing online services that were previously exclusively accessible offline.

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